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Julian Nöhl

Publications and source records attributed to Julian Nöhl.

3 recordsLinked to original sources

Life cycle assessment for all organic chemicals

Chemicals are embedded in nearly every aspect of modern society, yet their production poses substantial sustainability concerns. Achieving a sustainable chemical industry requires detailed Life Cycle Assessment (LCA); however, current assessments face many unknowns due to limited, partly inconsistent, and untransparent data coverage since existing Life Cycle Inventory (LCI) databases account for only a tiny fraction of traded chemicals. Here, we introduce the Chemical RetrosYnthesiS for Transparent Assessment of Life-cycles (CRYSTAL) framework, which automatically generates consistent and transparent LCI data for organic chemicals based on their molecular structure using retrosynthesis and machine-learned gate-to-gate inventories. Using the predictive power of CRYSTAL, we create a consistent database for more than 70000 organic chemicals, comprising over 110000 transparent LCI datasets that quantify both feedstock and energy demands, together with associated auxiliary materials, biosphere flows, and waste flows. From this comprehensive database, we identify 50 key environmental hotspots driving high impacts of organic chemical production across multiple environmental categories and pivotal hub chemicals that are most critical for downstream chemical production. In providing this comprehensive data foundation, the CRYSTAL framework offers systematic guidance for targeted engineering and policy interventions. Its transparent, modular nature is designed to shift chemical LCA from a reliance on "unknown unknowns" to a collaboratively improvable mapping of "known unknowns".

physics.chem-ph↗

Assessing Confidence in AI-Assisted Grading of Physics Exams through Psychometrics: An Exploratory Study

This study explores the use of artificial intelligence in grading high-stakes physics exams, emphasizing the application of psychometric methods, particularly Item Response Theory (IRT), to evaluate the reliability of AI-assisted grading. We examine how grading rubrics can be iteratively refined and how threshold parameters can determine when AI-generated grades are reliable versus when human intervention is necessary. By adjusting thresholds for correctness measures and uncertainty, AI can grade with high precision, significantly reducing grading workloads while maintaining accuracy. Our findings show that AI can achieve a coefficient of determination of $R^2\approx 0.91$ when handling half of the grading load, and $R^2 \approx 0.96$ for one-fifth of the load. These results demonstrate AI's potential to assist in grading large-scale assessments, reducing both human effort and associated costs. However, the study underscores the importance of human oversight in cases of uncertainty or complex problem-solving, ensuring the integrity of the grading process.

physics.ed-ph↗

Grading Assistance for a Handwritten Thermodynamics Exam using Artificial Intelligence: An Exploratory Study

Using a high-stakes thermodynamics exam as sample (252~students, four multipart problems), we investigate the viability of four workflows for AI-assisted grading of handwritten student solutions. We find that the greatest challenge lies in converting handwritten answers into a machine-readable format. The granularity of grading criteria also influences grading performance: employing a fine-grained rubric for entire problems often leads to bookkeeping errors and grading failures, while grading problems in parts is more reliable but tends to miss nuances. We also found that grading hand-drawn graphics, such as process diagrams, is less reliable than mathematical derivations due to the difficulty in differentiating essential details from extraneous information. Although the system is precise in identifying exams that meet passing criteria, exams with failing grades still require human grading. We conclude with recommendations to overcome some of the encountered challenges.

physics.ed-ph↗